Next Article in Journal
GIS-Based Visibility Network and Defensibility Model to Reconstruct Defensive System of the Han Dynasty in Central Xinjiang, China
Previous Article in Journal
Spatial Variation Relationship between Floating Population and Residential Burglary: A Case Study from ZG, China
Article Menu
Issue 8 (August) cover image

Export Article

Open AccessArticle
ISPRS Int. J. Geo-Inf. 2017, 6(8), 248; doi:10.3390/ijgi6080248

A Generalized Additive Model Combining Principal Component Analysis for PM2.5 Concentration Estimation

1
College of Geomatics, Shandong University of Science and Technology, Qingdao 266590, China
2
National Geographic Conditions Monitoring Research Center, Chinese Academy of Surveying and Mapping, Beijing 100830, China
3
School of Geosciences and Info-Physics, Central South University, Hunan 410083, China
*
Author to whom correspondence should be addressed.
Received: 25 June 2017 / Revised: 3 August 2017 / Accepted: 10 August 2017 / Published: 13 August 2017
View Full-Text   |   Download PDF [4182 KB, uploaded 14 August 2017]   |  

Abstract

As an extension of the traditional Land Use Regression (LUR) modelling, the generalized additive model (GAM) was developed in recent years to explore the non-linear relationships between PM2.5 concentrations and the factors impacting it. However, these studies did not consider the loss of information regarding predictor variables. To address this challenge, a generalized additive model combining principal component analysis (PCA–GAM) was proposed to estimate PM2.5 concentrations in this study. The reliability of PCA–GAM for estimating PM2.5 concentrations was tested in the Beijing-Tianjin-Hebei (BTH) region over a one-year period as a case study. The results showed that PCA–GAM outperforms traditional LUR modelling with relatively higher adjusted R2 (0.94) and lower RMSE (4.08 µg/m3). The CV-adjusted R2 (0.92) is high and close to the model-adjusted R2, proving the robustness of the PCA–GAM model. The PCA–GAM model enhances PM2.5 estimate accuracy by improving the usage of the effective predictor variables. Therefore, it can be concluded that PCA–GAM is a promising method for air pollution mapping and could be useful for decision makers taking a series of measures to combat air pollution. View Full-Text
Keywords: PCA; GAM; PM2.5 concentrations; effective predictor variables; utilization rate PCA; GAM; PM2.5 concentrations; effective predictor variables; utilization rate
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Li, S.; Zhai, L.; Zou, B.; Sang, H.; Fang, X. A Generalized Additive Model Combining Principal Component Analysis for PM2.5 Concentration Estimation. ISPRS Int. J. Geo-Inf. 2017, 6, 248.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
ISPRS Int. J. Geo-Inf. EISSN 2220-9964 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top